In this paper, we propose a novel finetuning algorithm for the recently introduced multi-way,
mulitlingual neural machine translate that enables zero-resource machine translation. When …

In statistical machine translation (SMT), the optimization of the system parameters to
maximize translation accuracy is now a fundamental part of virtually all modern systems. In …

Techniques are disclosed for optimizing results output by a natural language processing
system. For example, a method comprises optimizing one or more parameters of a natural …

In statistical machine translation, minimum error rate training (MERT) is a standard method
for tuning a single weight with regard to a given development data. However, due to the …

Minimum error rate training is a crucial component to many state-of-the-art NLP applications,
such as machine translation and speech recognition. However, common evaluation …

We define a new algorithm, named “Drem”, for tuning the weighted linear model in a
statistical machine translation system. Drem has two major innovations. First, it uses scaled …

In this paper, we survey and analyze state-of-the-art statistical machine translation (SMT)
techniques for speech translation (ST). We review key learning problems, and investigate …

In this paper, an automated method is proposed for optimising the real-valued parameters of
a hybrid Machine Translation (MT) system that employs pattern recognition techniques …

This paper presents the results of the WMT16 Tuning Shared Task. We provided the
participants of this task with a complete machine translation system and asked them to tune …

Minimum error rate training is often the preferred method for optimizing parameters of
statistical machine translation systems. MERT minimizes error rate by using a surrogate …